Gao Xunzhang, Liu Zhen, Chen Haowen, Li Xiang
College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2015 Jan 26;15(2):2723-36. doi: 10.3390/s150202723.
In existing sparsity-driven inverse synthetic aperture radar (ISAR) imaging framework a sparse recovery (SR) algorithm is usually applied to azimuth compression to achieve high resolution in the cross-range direction. For range compression, however, direct application of an SR algorithm is not very effective because the scattering centers resolved in the high resolution range profiles at different view angles always exhibit irregular range cell migration (RCM), especially for complex targets, which will blur the ISAR image. To alleviate the sparse recovery-induced RCM in range compression, a sparsity-driven framework for ISAR imaging named Fourier-sparsity integrated (FSI) method is proposed in this paper, which can simultaneously achieve better focusing performance in both the range and cross-range domains. Experiments using simulated data and real data demonstrate the superiority of our proposed framework over existing sparsity-driven methods and range-Doppler methods.
在现有的稀疏驱动逆合成孔径雷达（ISAR）成像框架中，通常应用稀疏恢复（SR）算法进行方位向压缩，以在距离向实现高分辨率。然而，对于距离压缩，直接应用SR算法效果不佳，因为在不同视角下的高分辨率距离像中分辨出的散射中心总是呈现不规则的距离单元徙动（RCM），特别是对于复杂目标，这会使ISAR图像模糊。为了减轻距离压缩中由稀疏恢复引起的RCM，本文提出了一种用于ISAR成像的稀疏驱动框架，即傅里叶-稀疏积分（FSI）方法，该方法可以在距离域和方位向域同时实现更好的聚焦性能。使用模拟数据和真实数据进行的实验证明了我们提出的框架相对于现有稀疏驱动方法和距离-多普勒方法的优越性。
